scholarly journals Prediction of Groundwater Level Changes Using Hybrid Wavelet Self- Adaptive Extreme Learning Machine Model- Observation Well of Sarab Qanbar, Kermanshah

2020 ◽  
Vol 23 (04) ◽  
Keyword(s):  
Extreme Learning Machine ◽  
Groundwater Level ◽  
Observation Well ◽  
Machine Model ◽  
Learning Machine ◽  
Groundwater Level Changes ◽  
Self Adaptive

scholarly journals Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Pak Kin Wong ◽  
Hang Cheong Wong ◽  
Chi Man Vong ◽  
Tong Meng Iong ◽  
Ka In Wong ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Extreme Learning Machine ◽  
External Disturbance ◽  
Feedback Signal ◽  
Ratio Model ◽  
Control Performance ◽  
Machine Model ◽  
Target Values ◽  
Lambda Sensor ◽  
Learning Machine

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.

Using enhanced crow search algorithm optimization-extreme learning machine model to forecast short-term wind power

2021 ◽  
pp. 115579
Author(s):  
Ling-Ling Li ◽  
Zhi-Feng Liu ◽  
Ming-Lang Tseng ◽  
Korbkul Jantarakolica ◽  
Ming K. Lim
Keyword(s):  
Wind Power ◽  
Extreme Learning Machine ◽  
Search Algorithm ◽  
Algorithm Optimization ◽  
Short Term ◽  
Machine Model ◽  
Learning Machine

Preference-driven yield-and-quality optimization for high-sulfur gas sweetening process by extreme learning machine model

2018 ◽  
Vol 22 (S3) ◽  
pp. 6371-6381
Author(s):  
Jianfeng Shang ◽  
Xiaohua Gu ◽  
Liping Yang ◽  
Haihong Tang ◽  
Kun Zhang ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Machine Model ◽  
Quality Optimization ◽  
Yield And Quality ◽  
Gas Sweetening ◽  
Learning Machine

scholarly journals Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4033
Author(s):  
Jonas Bielskus ◽  
Violeta Motuzienė ◽  
Tatjana Vilutienė ◽  
Audrius Indriulionis
Keyword(s):  
Energy Efficiency ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Open Space ◽  
Prediction Models ◽  
Co2 Concentration ◽  
Full Potential ◽  
Machine Model ◽  
Learning Machine ◽  
Occupancy Prediction

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

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